Overview

Dataset statistics

Number of variables50
Number of observations71412
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory163.8 MiB
Average record size in memory2.3 KiB

Variable types

CAT37
NUM11
BOOL2

Warnings

examide has constant value "71412" Constant
citoglipton has constant value "71412" Constant
metformin.pioglitazone has constant value "71412" Constant
patient_nbr has a high cardinality: 54183 distinct values High cardinality
medical_specialty has a high cardinality: 70 distinct values High cardinality
diag_1 has a high cardinality: 684 distinct values High cardinality
diag_2 has a high cardinality: 701 distinct values High cardinality
diag_3 has a high cardinality: 748 distinct values High cardinality
number_emergency is highly skewed (γ1 = 25.64811755) Skewed
patient_nbr is uniformly distributed Uniform
encounter_id has unique values Unique
num_procedures has 32777 (45.9%) zeros Zeros
number_outpatient has 59713 (83.6%) zeros Zeros
number_emergency has 63386 (88.8%) zeros Zeros
number_inpatient has 47350 (66.3%) zeros Zeros

Reproduction

Analysis started2020-12-10 02:58:34.963714
Analysis finished2020-12-10 03:00:10.599786
Duration1 minute and 35.64 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

encounter_id
Categorical

UNIQUE

Distinct71412
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
wyi19WqXkuxS8onGAm63
 
1
hLcKOEJuIqrn6XtlSFMf
 
1
JYdR5oVwGpLN679fxSOj
 
1
MhwmjJDEyoe3X6GFIQV7
 
1
xouPmCB0qA3L2vhUsYTF
 
1
Other values (71407)
71407 
ValueCountFrequency (%) 
wyi19WqXkuxS8onGAm631< 0.1%
 
hLcKOEJuIqrn6XtlSFMf1< 0.1%
 
JYdR5oVwGpLN679fxSOj1< 0.1%
 
MhwmjJDEyoe3X6GFIQV71< 0.1%
 
xouPmCB0qA3L2vhUsYTF1< 0.1%
 
WeC86jgItZFsdRibhqol1< 0.1%
 
g0hXptZ9s4CRMaf7AlNV1< 0.1%
 
QgIaTc2jwGXxrHC8y93F1< 0.1%
 
jF3rpPInZiVqGYouAf0m1< 0.1%
 
WbHPhTvsd3xcJ297pBzZ1< 0.1%
 
HtPMQdJeI4Uq0VDysbCL1< 0.1%
 
foZEJB0YAh5n9z3y8I2V1< 0.1%
 
n13Aye7F5E6KVW4ZfClt1< 0.1%
 
gbSN4jtsDdfqLkcHroAZ1< 0.1%
 
fvlkJZdOiIsPznBy8Gw41< 0.1%
 
aQtywvPAJzO4R5lUY0gZ1< 0.1%
 
auN4dqh1ZfoyimS0EMGY1< 0.1%
 
v3jdO1I2t4QRMZbCrGHl1< 0.1%
 
8KotTuY0bHMgNaleVm5y1< 0.1%
 
roVXNIqWxLQ1YgyJwT971< 0.1%
 
uQHJYwt7dD3lxkbnUVcy1< 0.1%
 
UFsqlkea6iIhbXpxz2Ad1< 0.1%
 
u5kBD2dZolaeLInrOYpH1< 0.1%
 
owBXRLuZpx4rlqUmY9fD1< 0.1%
 
6wHjPF0ax1io94NDeft51< 0.1%
 
Other values (71387)71387> 99.9%
 
2020-12-10T03:00:11.140964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique71412 ?
Unique (%)100.0%
2020-12-10T03:00:11.411992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length20
Mean length20
Min length20

Overview of Unicode Properties

Unique unicode characters62
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
H233301.6%
 
n232631.6%
 
z232431.6%
 
g232131.6%
 
6231911.6%
 
N231671.6%
 
E231631.6%
 
h231591.6%
 
T231541.6%
 
c231521.6%
 
K231511.6%
 
Q231481.6%
 
Y231361.6%
 
U231321.6%
 
r231291.6%
 
S231181.6%
 
B231081.6%
 
W231021.6%
 
q230971.6%
 
m230921.6%
 
u230861.6%
 
G230821.6%
 
Z230771.6%
 
D230771.6%
 
i230771.6%
 
Other values (37)84959359.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter59967342.0%
 
Lowercase Letter59872641.9%
 
Decimal Number22984116.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n232633.9%
 
z232433.9%
 
g232133.9%
 
h231593.9%
 
c231523.9%
 
r231293.9%
 
q230973.9%
 
m230923.9%
 
u230863.9%
 
i230773.9%
 
p230663.9%
 
b230203.8%
 
o230173.8%
 
y230163.8%
 
l230093.8%
 
s230003.8%
 
f229843.8%
 
w229663.8%
 
v229663.8%
 
e229593.8%
 
k229503.8%
 
x229403.8%
 
a229343.8%
 
j228853.8%
 
d227883.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H233303.9%
 
N231673.9%
 
E231633.9%
 
T231543.9%
 
K231513.9%
 
Q231483.9%
 
Y231363.9%
 
U231323.9%
 
S231183.9%
 
B231083.9%
 
W231023.9%
 
G230823.8%
 
Z230773.8%
 
D230773.8%
 
V230513.8%
 
X230443.8%
 
J230263.8%
 
I230263.8%
 
F230233.8%
 
R229993.8%
 
O229863.8%
 
M229543.8%
 
A229473.8%
 
P229413.8%
 
C228713.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
62319110.1%
 
82307410.0%
 
42305310.0%
 
92299310.0%
 
52297710.0%
 
22297110.0%
 
12297010.0%
 
02293410.0%
 
32288110.0%
 
7227979.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin119839983.9%
 
Common22984116.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
H233301.9%
 
n232631.9%
 
z232431.9%
 
g232131.9%
 
N231671.9%
 
E231631.9%
 
h231591.9%
 
T231541.9%
 
c231521.9%
 
K231511.9%
 
Q231481.9%
 
Y231361.9%
 
U231321.9%
 
r231291.9%
 
S231181.9%
 
B231081.9%
 
W231021.9%
 
q230971.9%
 
m230921.9%
 
u230861.9%
 
G230821.9%
 
Z230771.9%
 
D230771.9%
 
i230771.9%
 
p230661.9%
 
Other values (27)61987751.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
62319110.1%
 
82307410.0%
 
42305310.0%
 
92299310.0%
 
52297710.0%
 
22297110.0%
 
12297010.0%
 
02293410.0%
 
32288110.0%
 
7227979.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1428240100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
H233301.6%
 
n232631.6%
 
z232431.6%
 
g232131.6%
 
6231911.6%
 
N231671.6%
 
E231631.6%
 
h231591.6%
 
T231541.6%
 
c231521.6%
 
K231511.6%
 
Q231481.6%
 
Y231361.6%
 
U231321.6%
 
r231291.6%
 
S231181.6%
 
B231081.6%
 
W231021.6%
 
q230971.6%
 
m230921.6%
 
u230861.6%
 
G230821.6%
 
Z230771.6%
 
D230771.6%
 
i230771.6%
 
Other values (37)84959359.5%
 

patient_nbr
Categorical

HIGH CARDINALITY
UNIFORM

Distinct54183
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
7AiVZ3o9MI
 
23
iqvyEtmTRV
 
20
X0eK46N8VZ
 
18
wBOGzKM65j
 
18
92weQmJP3k
 
18
Other values (54178)
71315 
ValueCountFrequency (%) 
7AiVZ3o9MI23< 0.1%
 
iqvyEtmTRV20< 0.1%
 
X0eK46N8VZ18< 0.1%
 
wBOGzKM65j18< 0.1%
 
92weQmJP3k18< 0.1%
 
WWNXiwfqer17< 0.1%
 
Srw72Qypuo16< 0.1%
 
kbonm1fuEf16< 0.1%
 
MxqJRVYeF716< 0.1%
 
Y7aUIjem3U15< 0.1%
 
diz8wWdWdV15< 0.1%
 
HMHys26CW814< 0.1%
 
DXoRQlNDoZ14< 0.1%
 
qf3f6vaseF14< 0.1%
 
A7NXuNVf5a14< 0.1%
 
IIRAHAN4u914< 0.1%
 
CGa4CdbETB14< 0.1%
 
YnfRhgs4Te13< 0.1%
 
Z76oHjdBWl13< 0.1%
 
9l1aWpYDKi13< 0.1%
 
I6FvPvHvOw13< 0.1%
 
Q5GP9h57M813< 0.1%
 
yz3Z5YwSXD13< 0.1%
 
RwPoYogLHR13< 0.1%
 
jwcca3mCwH12< 0.1%
 
Other values (54158)7103399.5%
 
2020-12-10T03:00:11.917452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43551 ?
Unique (%)61.0%
2020-12-10T03:00:12.207756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters62
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r118881.7%
 
3117961.7%
 
o117421.6%
 
P117201.6%
 
8116991.6%
 
A116891.6%
 
s116741.6%
 
e116611.6%
 
i116581.6%
 
G116431.6%
 
w116431.6%
 
f116331.6%
 
F116281.6%
 
2116281.6%
 
q116281.6%
 
h116181.6%
 
S116131.6%
 
g116111.6%
 
H116111.6%
 
0116091.6%
 
W116011.6%
 
T115931.6%
 
m115921.6%
 
R115891.6%
 
v115861.6%
 
Other values (37)42276759.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter29997942.0%
 
Uppercase Letter29873041.8%
 
Decimal Number11541116.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P117203.9%
 
A116893.9%
 
G116433.9%
 
F116283.9%
 
S116133.9%
 
H116113.9%
 
W116013.9%
 
T115933.9%
 
R115893.9%
 
U115433.9%
 
V115273.9%
 
N115223.9%
 
Y114993.8%
 
K114893.8%
 
I114643.8%
 
Q114213.8%
 
B114073.8%
 
E114053.8%
 
D113873.8%
 
C113753.8%
 
O113733.8%
 
X113713.8%
 
Z113533.8%
 
M113413.8%
 
J113033.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
31179610.2%
 
81169910.1%
 
21162810.1%
 
01160910.1%
 
51157810.0%
 
61153110.0%
 
71152310.0%
 
11148510.0%
 
4113849.9%
 
9111789.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r118884.0%
 
o117423.9%
 
s116743.9%
 
e116613.9%
 
i116583.9%
 
w116433.9%
 
f116333.9%
 
q116283.9%
 
h116183.9%
 
g116113.9%
 
m115923.9%
 
v115863.9%
 
j115843.9%
 
t115563.9%
 
z115393.8%
 
d115343.8%
 
b115323.8%
 
a115293.8%
 
k114543.8%
 
c114493.8%
 
p114263.8%
 
x113713.8%
 
y113373.8%
 
n113233.8%
 
u112703.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin59870983.8%
 
Common11541116.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r118882.0%
 
o117422.0%
 
P117202.0%
 
A116892.0%
 
s116741.9%
 
e116611.9%
 
i116581.9%
 
G116431.9%
 
w116431.9%
 
f116331.9%
 
F116281.9%
 
q116281.9%
 
h116181.9%
 
S116131.9%
 
g116111.9%
 
H116111.9%
 
W116011.9%
 
T115931.9%
 
m115921.9%
 
R115891.9%
 
v115861.9%
 
j115841.9%
 
t115561.9%
 
U115431.9%
 
z115391.9%
 
Other values (27)30786651.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
31179610.2%
 
81169910.1%
 
21162810.1%
 
01160910.1%
 
51157810.0%
 
61153110.0%
 
71152310.0%
 
11148510.0%
 
4113849.9%
 
9111789.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII714120100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r118881.7%
 
3117961.7%
 
o117421.6%
 
P117201.6%
 
8116991.6%
 
A116891.6%
 
s116741.6%
 
e116611.6%
 
i116581.6%
 
G116431.6%
 
w116431.6%
 
f116331.6%
 
F116281.6%
 
2116281.6%
 
q116281.6%
 
h116181.6%
 
S116131.6%
 
g116111.6%
 
H116111.6%
 
0116091.6%
 
W116011.6%
 
T115931.6%
 
m115921.6%
 
R115891.6%
 
v115861.6%
 
Other values (37)42276759.2%
 

race
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Caucasian
53293 
AfricanAmerican
13560 
?
 
1602
Hispanic
 
1404
Other
 
1093
ValueCountFrequency (%) 
Caucasian5329374.6%
 
AfricanAmerican1356019.0%
 
?16022.2%
 
Hispanic14042.0%
 
Other10931.5%
 
Asian4600.6%
 
2020-12-10T03:00:12.426794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:12.609954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:12.848265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length9
Mean length9.85318994
Min length1

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a18886326.8%
 
i8368111.9%
 
n8227711.7%
 
c8181711.6%
 
s551577.8%
 
C532937.6%
 
u532937.6%
 
r282134.0%
 
A275803.9%
 
e146532.1%
 
f135601.9%
 
m135601.9%
 
?16020.2%
 
H14040.2%
 
p14040.2%
 
O10930.2%
 
t10930.2%
 
h10930.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter61866487.9%
 
Uppercase Letter8337011.8%
 
Other Punctuation16020.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C5329363.9%
 
A2758033.1%
 
H14041.7%
 
O10931.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a18886330.5%
 
i8368113.5%
 
n8227713.3%
 
c8181713.2%
 
s551578.9%
 
u532938.6%
 
r282134.6%
 
e146532.4%
 
f135602.2%
 
m135602.2%
 
p14040.2%
 
t10930.2%
 
h10930.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?1602100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin70203499.8%
 
Common16020.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a18886326.9%
 
i8368111.9%
 
n8227711.7%
 
c8181711.7%
 
s551577.9%
 
C532937.6%
 
u532937.6%
 
r282134.0%
 
A275803.9%
 
e146532.1%
 
f135601.9%
 
m135601.9%
 
H14040.2%
 
p14040.2%
 
O10930.2%
 
t10930.2%
 
h10930.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
?1602100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII703636100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a18886326.8%
 
i8368111.9%
 
n8227711.7%
 
c8181711.6%
 
s551577.8%
 
C532937.6%
 
u532937.6%
 
r282134.0%
 
A275803.9%
 
e146532.1%
 
f135601.9%
 
m135601.9%
 
?16020.2%
 
H14040.2%
 
p14040.2%
 
O10930.2%
 
t10930.2%
 
h10930.2%
 

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Female
38335 
Male
33074 
Unknown/Invalid
 
3
ValueCountFrequency (%) 
Female3833553.7%
 
Male3307446.3%
 
Unknown/Invalid3< 0.1%
 
2020-12-10T03:00:13.168034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:13.329720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:13.502045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length6
Mean length5.074091189
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e10974430.3%
 
a7141219.7%
 
l7141219.7%
 
F3833510.6%
 
m3833510.6%
 
M330749.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
/3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter29093380.3%
 
Uppercase Letter7141519.7%
 
Other Punctuation3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F3833553.7%
 
M3307446.3%
 
U3< 0.1%
 
I3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e10974437.7%
 
a7141224.5%
 
l7141224.5%
 
m3833513.2%
 
n12< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin362348> 99.9%
 
Common3< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e10974430.3%
 
a7141219.7%
 
l7141219.7%
 
F3833510.6%
 
m3833510.6%
 
M330749.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/3100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII362351100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e10974430.3%
 
a7141219.7%
 
l7141219.7%
 
F3833510.6%
 
m3833510.6%
 
M330749.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
/3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
[70-80)
18145 
[60-70)
15842 
[50-60)
12234 
[80-90)
11988 
[40-50)
6818 
Other values (5)
6385 
ValueCountFrequency (%) 
[70-80)1814525.4%
 
[60-70)1584222.2%
 
[50-60)1223417.1%
 
[80-90)1198816.8%
 
[40-50)68189.5%
 
[30-40)26943.8%
 
[90-100)19602.7%
 
[20-30)11291.6%
 
[10-20)5010.7%
 
[0-10)1010.1%
 
2020-12-10T03:00:13.817758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:13.981013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:14.274135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length7.026032039
Min length6

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
014478428.9%
 
[7141214.2%
 
-7141214.2%
 
)7141214.2%
 
7339876.8%
 
8301336.0%
 
6280765.6%
 
5190523.8%
 
9139482.8%
 
495121.9%
 
338230.8%
 
125620.5%
 
216300.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number28750757.3%
 
Open Punctuation7141214.2%
 
Dash Punctuation7141214.2%
 
Close Punctuation7141214.2%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[71412100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
014478450.4%
 
73398711.8%
 
83013310.5%
 
6280769.8%
 
5190526.6%
 
9139484.9%
 
495123.3%
 
338231.3%
 
125620.9%
 
216300.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-71412100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)71412100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common501743100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
014478428.9%
 
[7141214.2%
 
-7141214.2%
 
)7141214.2%
 
7339876.8%
 
8301336.0%
 
6280765.6%
 
5190523.8%
 
9139482.8%
 
495121.9%
 
338230.8%
 
125620.5%
 
216300.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII501743100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
014478428.9%
 
[7141214.2%
 
-7141214.2%
 
)7141214.2%
 
7339876.8%
 
8301336.0%
 
6280765.6%
 
5190523.8%
 
9139482.8%
 
495121.9%
 
338230.8%
 
125620.5%
 
216300.3%
 

weight
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
?
69195 
[75-100)
 
915
[50-75)
 
627
[100-125)
 
443
[125-150)
 
102
Other values (5)
 
130
ValueCountFrequency (%) 
?6919596.9%
 
[75-100)9151.3%
 
[50-75)6270.9%
 
[100-125)4430.6%
 
[125-150)1020.1%
 
[25-50)650.1%
 
[0-25)33< 0.1%
 
[150-175)24< 0.1%
 
[175-200)7< 0.1%
 
>2001< 0.1%
 
2020-12-10T03:00:14.503931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:14.661443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:14.923012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length1
Mean length1.214711813
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories6 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
?6919579.8%
 
035834.1%
 
530343.5%
 
[22162.6%
 
-22162.6%
 
)22162.6%
 
120602.4%
 
715731.8%
 
26510.8%
 
>1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Other Punctuation6919579.8%
 
Decimal Number1090112.6%
 
Open Punctuation22162.6%
 
Dash Punctuation22162.6%
 
Close Punctuation22162.6%
 
Math Symbol1< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?69195100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[2216100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0358332.9%
 
5303427.8%
 
1206018.9%
 
7157314.4%
 
26516.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2216100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)2216100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common86745100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
?6919579.8%
 
035834.1%
 
530343.5%
 
[22162.6%
 
-22162.6%
 
)22162.6%
 
120602.4%
 
715731.8%
 
26510.8%
 
>1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII86745100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
?6919579.8%
 
035834.1%
 
530343.5%
 
[22162.6%
 
-22162.6%
 
)22162.6%
 
120602.4%
 
715731.8%
 
26510.8%
 
>1< 0.1%
 

admission_type_id
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.022993334
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:15.843796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.444982031
Coefficient of variation (CV)0.7142791855
Kurtosis1.972866464
Mean2.022993334
Median Absolute Deviation (MAD)0
Skewness1.597293289
Sum144466
Variance2.087973071
MonotocityNot monotonic
2020-12-10T03:00:16.043888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
13790353.1%
 
31326518.6%
 
21295818.1%
 
636965.2%
 
533354.7%
 
82340.3%
 
715< 0.1%
 
46< 0.1%
 
ValueCountFrequency (%) 
13790353.1%
 
21295818.1%
 
31326518.6%
 
46< 0.1%
 
533354.7%
 
636965.2%
 
715< 0.1%
 
82340.3%
 
ValueCountFrequency (%) 
82340.3%
 
715< 0.1%
 
636965.2%
 
533354.7%
 
46< 0.1%
 
31326518.6%
 
21295818.1%
 
13790353.1%
 

discharge_disposition_id
Real number (ℝ≥0)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.71965496
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:16.283808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.284452531
Coefficient of variation (CV)1.420683528
Kurtosis5.974928797
Mean3.71965496
Median Absolute Deviation (MAD)0
Skewness2.558211713
Sum265628
Variance27.92543855
MonotocityNot monotonic
2020-12-10T03:00:16.508055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
14228259.2%
 
3971513.6%
 
6905712.7%
 
1825893.6%
 
214982.1%
 
2214142.0%
 
1111541.6%
 
58361.2%
 
256811.0%
 
45990.8%
 
74400.6%
 
232920.4%
 
132840.4%
 
142680.4%
 
281000.1%
 
8760.1%
 
15500.1%
 
2433< 0.1%
 
913< 0.1%
 
179< 0.1%
 
198< 0.1%
 
166< 0.1%
 
274< 0.1%
 
103< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
14228259.2%
 
214982.1%
 
3971513.6%
 
45990.8%
 
58361.2%
 
6905712.7%
 
74400.6%
 
8760.1%
 
913< 0.1%
 
103< 0.1%
 
ValueCountFrequency (%) 
281000.1%
 
274< 0.1%
 
256811.0%
 
2433< 0.1%
 
232920.4%
 
2214142.0%
 
198< 0.1%
 
1825893.6%
 
179< 0.1%
 
166< 0.1%
 

admission_source_id
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.753710861
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:16.704864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.068028362
Coefficient of variation (CV)0.7070269014
Kurtosis1.742458086
Mean5.753710861
Median Absolute Deviation (MAD)0
Skewness1.031069588
Sum410884
Variance16.54885475
MonotocityNot monotonic
2020-12-10T03:00:16.965451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
74033556.5%
 
12076229.1%
 
1747686.7%
 
422013.1%
 
615912.2%
 
28081.1%
 
55860.8%
 
31380.2%
 
201130.2%
 
9760.1%
 
812< 0.1%
 
229< 0.1%
 
106< 0.1%
 
112< 0.1%
 
142< 0.1%
 
252< 0.1%
 
131< 0.1%
 
ValueCountFrequency (%) 
12076229.1%
 
28081.1%
 
31380.2%
 
422013.1%
 
55860.8%
 
615912.2%
 
74033556.5%
 
812< 0.1%
 
9760.1%
 
106< 0.1%
 
ValueCountFrequency (%) 
252< 0.1%
 
229< 0.1%
 
201130.2%
 
1747686.7%
 
142< 0.1%
 
131< 0.1%
 
112< 0.1%
 
106< 0.1%
 
9760.1%
 
812< 0.1%
 

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.384753263
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:17.231827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.979709285
Coefficient of variation (CV)0.67956145
Kurtosis0.8536709844
Mean4.384753263
Median Absolute Deviation (MAD)2
Skewness1.135714776
Sum313124
Variance8.878667423
MonotocityNot monotonic
2020-12-10T03:00:17.464049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
31240717.4%
 
21217017.0%
 
11001914.0%
 
4981113.7%
 
569829.8%
 
652737.4%
 
740825.7%
 
830334.2%
 
921233.0%
 
1016402.3%
 
1113161.8%
 
1210081.4%
 
138371.2%
 
147111.0%
 
ValueCountFrequency (%) 
11001914.0%
 
21217017.0%
 
31240717.4%
 
4981113.7%
 
569829.8%
 
652737.4%
 
740825.7%
 
830334.2%
 
921233.0%
 
1016402.3%
 
ValueCountFrequency (%) 
147111.0%
 
138371.2%
 
1210081.4%
 
1113161.8%
 
1016402.3%
 
921233.0%
 
830334.2%
 
740825.7%
 
652737.4%
 
569829.8%
 

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
?
28178 
MC
22715 
HM
4357 
SP
3545 
BC
3293 
Other values (13)
9324 
ValueCountFrequency (%) 
?2817839.5%
 
MC2271531.8%
 
HM43576.1%
 
SP35455.0%
 
BC32934.6%
 
MD24493.4%
 
CP18212.5%
 
UN17512.5%
 
CM13761.9%
 
OG7441.0%
 
PO4150.6%
 
DM3850.5%
 
CH1090.2%
 
WC980.1%
 
OT700.1%
 
MP600.1%
 
SI450.1%
 
FR1< 0.1%
 
2020-12-10T03:00:17.704565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:17.924831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.605416457
Min length1

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M3134227.3%
 
C2941225.7%
 
?2817824.6%
 
P58415.1%
 
H44663.9%
 
S35903.1%
 
B32932.9%
 
D28342.5%
 
U17511.5%
 
N17511.5%
 
O12291.1%
 
G7440.6%
 
W980.1%
 
T700.1%
 
I45< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter8646875.4%
 
Other Punctuation2817824.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M3134236.2%
 
C2941234.0%
 
P58416.8%
 
H44665.2%
 
S35904.2%
 
B32933.8%
 
D28343.3%
 
U17512.0%
 
N17512.0%
 
O12291.4%
 
G7440.9%
 
W980.1%
 
T700.1%
 
I450.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?28178100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8646875.4%
 
Common2817824.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M3134236.2%
 
C2941234.0%
 
P58416.8%
 
H44665.2%
 
S35904.2%
 
B32933.8%
 
D28343.3%
 
U17512.0%
 
N17512.0%
 
O12291.4%
 
G7440.9%
 
W980.1%
 
T700.1%
 
I450.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
?28178100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII114646100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M3134227.3%
 
C2941225.7%
 
?2817824.6%
 
P58415.1%
 
H44663.9%
 
S35903.1%
 
B32932.9%
 
D28342.5%
 
U17511.5%
 
N17511.5%
 
O12291.1%
 
G7440.6%
 
W980.1%
 
T700.1%
 
I45< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

medical_specialty
Categorical

HIGH CARDINALITY

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
?
35120 
InternalMedicine
10247 
Emergency/Trauma
5291 
Family/GeneralPractice
5269 
Cardiology
3756 
Other values (65)
11729 
ValueCountFrequency (%) 
?3512049.2%
 
InternalMedicine1024714.3%
 
Emergency/Trauma52917.4%
 
Family/GeneralPractice52697.4%
 
Cardiology37565.3%
 
Surgery-General21693.0%
 
Nephrology11081.6%
 
Orthopedics9811.4%
 
Orthopedics-Reconstructive8791.2%
 
Radiologist7941.1%
 
Pulmonology5990.8%
 
Psychiatry5900.8%
 
Urology5020.7%
 
ObstetricsandGynecology4690.7%
 
Surgery-Cardiovascular/Thoracic4680.7%
 
Gastroenterology3820.5%
 
Surgery-Vascular3520.5%
 
Surgery-Neuro3210.4%
 
PhysicalMedicineandRehabilitation2740.4%
 
Oncology2460.3%
 
Pediatrics1890.3%
 
Hematology/Oncology1630.2%
 
Neurology1280.2%
 
Pediatrics-Endocrinology1060.1%
 
Otolaryngology920.1%
 
Other values (45)9171.3%
 
2020-12-10T03:00:18.197560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7 ?
Unique (%)< 0.1%
2020-12-10T03:00:18.504137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length8
Mean length8.60930936
Min length1

Overview of Unicode Properties

Unique unicode characters42
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e7373912.0%
 
r538668.8%
 
a499688.1%
 
n482107.8%
 
i444567.2%
 
c351715.7%
 
?351205.7%
 
l343295.6%
 
y244824.0%
 
t239813.9%
 
o238493.9%
 
d189383.1%
 
g178982.9%
 
m167582.7%
 
u117351.9%
 
/111941.8%
 
M105451.7%
 
I102771.7%
 
G83671.4%
 
s74211.2%
 
P73571.2%
 
T58320.9%
 
E54870.9%
 
F52770.9%
 
h48670.8%
 
Other values (17)256844.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter49499680.5%
 
Uppercase Letter6884811.2%
 
Other Punctuation463387.5%
 
Dash Punctuation46260.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1054515.3%
 
I1027714.9%
 
G836712.2%
 
P735710.7%
 
T58328.5%
 
E54878.0%
 
F52777.7%
 
C44286.4%
 
S35785.2%
 
O29434.3%
 
R20022.9%
 
N15742.3%
 
U5020.7%
 
V3520.5%
 
H2600.4%
 
A350.1%
 
D32< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e7373914.9%
 
r5386610.9%
 
a4996810.1%
 
n482109.7%
 
i444569.0%
 
c351717.1%
 
l343296.9%
 
y244824.9%
 
t239814.8%
 
o238494.8%
 
d189383.8%
 
g178983.6%
 
m167583.4%
 
u117352.4%
 
s74211.5%
 
h48671.0%
 
p30820.6%
 
v14200.3%
 
b7810.2%
 
f34< 0.1%
 
x11< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?3512075.8%
 
/1119424.2%
 
&240.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4626100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin56384491.7%
 
Common509648.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e7373913.1%
 
r538669.6%
 
a499688.9%
 
n482108.6%
 
i444567.9%
 
c351716.2%
 
l343296.1%
 
y244824.3%
 
t239814.3%
 
o238494.2%
 
d189383.4%
 
g178983.2%
 
m167583.0%
 
u117352.1%
 
M105451.9%
 
I102771.8%
 
G83671.5%
 
s74211.3%
 
P73571.3%
 
T58321.0%
 
E54871.0%
 
F52770.9%
 
h48670.9%
 
C44280.8%
 
S35780.6%
 
Other values (13)130282.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
?3512068.9%
 
/1119422.0%
 
-46269.1%
 
&24< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII614808100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e7373912.0%
 
r538668.8%
 
a499688.1%
 
n482107.8%
 
i444567.2%
 
c351715.7%
 
?351205.7%
 
l343295.6%
 
y244824.0%
 
t239813.9%
 
o238493.9%
 
d189383.1%
 
g178982.9%
 
m167582.7%
 
u117351.9%
 
/111941.8%
 
M105451.7%
 
I102771.7%
 
G83671.4%
 
s74211.2%
 
P73571.2%
 
T58320.9%
 
E54870.9%
 
F52770.9%
 
h48670.8%
 
Other values (17)256844.2%
 

num_lab_procedures
Real number (ℝ≥0)

Distinct116
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.06697754
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:18.747972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.65204974
Coefficient of variation (CV)0.4563136506
Kurtosis-0.2487677647
Mean43.06697754
Median Absolute Deviation (MAD)13
Skewness-0.2400051788
Sum3075499
Variance386.203059
MonotocityNot monotonic
2020-12-10T03:00:19.056115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
122833.2%
 
4319762.8%
 
4417682.5%
 
4516942.4%
 
3815612.2%
 
4615562.2%
 
4015362.2%
 
4714792.1%
 
4214782.1%
 
4114582.0%
 
4914552.0%
 
3714272.0%
 
3914192.0%
 
3613992.0%
 
4813982.0%
 
5013541.9%
 
5113441.9%
 
3513271.9%
 
5213091.8%
 
5612971.8%
 
5412911.8%
 
5512821.8%
 
5312481.7%
 
5712161.7%
 
5812141.7%
 
Other values (91)3464348.5%
 
ValueCountFrequency (%) 
122833.2%
 
27601.1%
 
34680.7%
 
42520.4%
 
52000.3%
 
61950.3%
 
72230.3%
 
82530.4%
 
96230.9%
 
105820.8%
 
ValueCountFrequency (%) 
1321< 0.1%
 
1291< 0.1%
 
1261< 0.1%
 
1211< 0.1%
 
1181< 0.1%
 
1141< 0.1%
 
1131< 0.1%
 
1113< 0.1%
 
1093< 0.1%
 
1082< 0.1%
 

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.340741052
Minimum0
Maximum6
Zeros32777
Zeros (%)45.9%
Memory size1.1 MiB
2020-12-10T03:00:19.312183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.710199979
Coefficient of variation (CV)1.275563224
Kurtosis0.8544174175
Mean1.340741052
Median Absolute Deviation (MAD)1
Skewness1.318756524
Sum95745
Variance2.924783969
MonotocityNot monotonic
2020-12-10T03:00:19.524272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
03277745.9%
 
11454520.4%
 
2894412.5%
 
364909.1%
 
635214.9%
 
429594.1%
 
521763.0%
 
ValueCountFrequency (%) 
03277745.9%
 
11454520.4%
 
2894412.5%
 
364909.1%
 
429594.1%
 
521763.0%
 
635214.9%
 
ValueCountFrequency (%) 
635214.9%
 
521763.0%
 
429594.1%
 
364909.1%
 
2894412.5%
 
11454520.4%
 
03277745.9%
 

num_medications
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.03568028
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:19.769644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.141536786
Coefficient of variation (CV)0.5077138384
Kurtosis3.60322821
Mean16.03568028
Median Absolute Deviation (MAD)5
Skewness1.34923898
Sum1145140
Variance66.28462123
MonotocityNot monotonic
2020-12-10T03:00:20.060007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1343216.1%
 
1241585.8%
 
1141225.8%
 
1440865.7%
 
1540115.6%
 
1638695.4%
 
1036945.2%
 
1734704.9%
 
934444.8%
 
1831804.5%
 
830364.3%
 
1928584.0%
 
2025793.6%
 
724323.4%
 
2122563.2%
 
2220402.9%
 
619272.7%
 
2316772.3%
 
2414472.0%
 
513741.9%
 
2513391.9%
 
2611341.6%
 
2710201.4%
 
49741.4%
 
288331.2%
 
Other values (50)61318.6%
 
ValueCountFrequency (%) 
11930.3%
 
23110.4%
 
36390.9%
 
49741.4%
 
513741.9%
 
619272.7%
 
724323.4%
 
830364.3%
 
934444.8%
 
1036945.2%
 
ValueCountFrequency (%) 
811< 0.1%
 
791< 0.1%
 
751< 0.1%
 
741< 0.1%
 
721< 0.1%
 
702< 0.1%
 
695< 0.1%
 
686< 0.1%
 
676< 0.1%
 
665< 0.1%
 

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.367879348
Minimum0
Maximum40
Zeros59713
Zeros (%)83.6%
Memory size1.1 MiB
2020-12-10T03:00:20.324765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.244255212
Coefficient of variation (CV)3.38223719
Kurtosis121.5520298
Mean0.367879348
Median Absolute Deviation (MAD)0
Skewness8.097768529
Sum26271
Variance1.548171033
MonotocityNot monotonic
2020-12-10T03:00:20.568147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
05971383.6%
 
159648.4%
 
224903.5%
 
314422.0%
 
47741.1%
 
53660.5%
 
62240.3%
 
71080.2%
 
8720.1%
 
9630.1%
 
10400.1%
 
1132< 0.1%
 
1420< 0.1%
 
1219< 0.1%
 
1319< 0.1%
 
1513< 0.1%
 
1613< 0.1%
 
177< 0.1%
 
184< 0.1%
 
204< 0.1%
 
214< 0.1%
 
193< 0.1%
 
223< 0.1%
 
362< 0.1%
 
242< 0.1%
 
Other values (8)11< 0.1%
 
ValueCountFrequency (%) 
05971383.6%
 
159648.4%
 
224903.5%
 
314422.0%
 
47741.1%
 
53660.5%
 
62240.3%
 
71080.2%
 
8720.1%
 
9630.1%
 
ValueCountFrequency (%) 
401< 0.1%
 
362< 0.1%
 
351< 0.1%
 
331< 0.1%
 
292< 0.1%
 
281< 0.1%
 
272< 0.1%
 
261< 0.1%
 
252< 0.1%
 
242< 0.1%
 

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1991542038
Minimum0
Maximum76
Zeros63386
Zeros (%)88.8%
Memory size1.1 MiB
2020-12-10T03:00:20.822731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9648107567
Coefficient of variation (CV)4.844541257
Kurtosis1394.312276
Mean0.1991542038
Median Absolute Deviation (MAD)0
Skewness25.64811755
Sum14222
Variance0.9308597963
MonotocityNot monotonic
2020-12-10T03:00:21.052761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
06338688.8%
 
154117.6%
 
214602.0%
 
34920.7%
 
42640.4%
 
51370.2%
 
6700.1%
 
7530.1%
 
835< 0.1%
 
1026< 0.1%
 
1116< 0.1%
 
915< 0.1%
 
138< 0.1%
 
126< 0.1%
 
225< 0.1%
 
165< 0.1%
 
204< 0.1%
 
183< 0.1%
 
143< 0.1%
 
192< 0.1%
 
152< 0.1%
 
641< 0.1%
 
631< 0.1%
 
541< 0.1%
 
241< 0.1%
 
Other values (5)5< 0.1%
 
ValueCountFrequency (%) 
06338688.8%
 
154117.6%
 
214602.0%
 
34920.7%
 
42640.4%
 
51370.2%
 
6700.1%
 
7530.1%
 
835< 0.1%
 
915< 0.1%
 
ValueCountFrequency (%) 
761< 0.1%
 
641< 0.1%
 
631< 0.1%
 
541< 0.1%
 
461< 0.1%
 
421< 0.1%
 
281< 0.1%
 
251< 0.1%
 
241< 0.1%
 
225< 0.1%
 

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6392903154
Minimum0
Maximum19
Zeros47350
Zeros (%)66.3%
Memory size1.1 MiB
2020-12-10T03:00:21.316054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.263836081
Coefficient of variation (CV)1.976936066
Kurtosis20.26940924
Mean0.6392903154
Median Absolute Deviation (MAD)0
Skewness3.578740264
Sum45653
Variance1.597281641
MonotocityNot monotonic
2020-12-10T03:00:21.603942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
04735066.3%
 
11369619.2%
 
253777.5%
 
324183.4%
 
411521.6%
 
55740.8%
 
63410.5%
 
71850.3%
 
81100.2%
 
9780.1%
 
10430.1%
 
1128< 0.1%
 
1224< 0.1%
 
1312< 0.1%
 
149< 0.1%
 
157< 0.1%
 
165< 0.1%
 
192< 0.1%
 
171< 0.1%
 
ValueCountFrequency (%) 
04735066.3%
 
11369619.2%
 
253777.5%
 
324183.4%
 
411521.6%
 
55740.8%
 
63410.5%
 
71850.3%
 
81100.2%
 
9780.1%
 
ValueCountFrequency (%) 
192< 0.1%
 
171< 0.1%
 
165< 0.1%
 
157< 0.1%
 
149< 0.1%
 
1312< 0.1%
 
1224< 0.1%
 
1128< 0.1%
 
10430.1%
 
9780.1%
 

diag_1
Categorical

HIGH CARDINALITY

Distinct684
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
428
 
4822
414
 
4601
786
 
2816
410
 
2492
486
 
2478
Other values (679)
54203 
ValueCountFrequency (%) 
42848226.8%
 
41446016.4%
 
78628163.9%
 
41024923.5%
 
48624783.5%
 
42719452.7%
 
49115842.2%
 
71515102.1%
 
78014212.0%
 
43414212.0%
 
68214172.0%
 
99613761.9%
 
27613561.9%
 
3812011.7%
 
250.811661.6%
 
59911271.6%
 
58410521.5%
 
V578541.2%
 
250.68461.2%
 
5188011.1%
 
5777391.0%
 
8207341.0%
 
4937221.0%
 
5627021.0%
 
4356971.0%
 
Other values (659)3153244.2%
 
2020-12-10T03:00:21.910883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique91 ?
Unique (%)0.1%
2020-12-10T03:00:22.215073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.175348681
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
43877517.1%
 
22796412.3%
 
82666911.8%
 
52605511.5%
 
7201628.9%
 
1196768.7%
 
0174977.7%
 
6163597.2%
 
9139816.2%
 
3124335.5%
 
.59872.6%
 
V11860.5%
 
?14< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number21957196.8%
 
Other Punctuation60012.6%
 
Uppercase Letter11860.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
43877517.7%
 
22796412.7%
 
82666912.1%
 
52605511.9%
 
7201629.2%
 
1196769.0%
 
0174978.0%
 
6163597.5%
 
9139816.4%
 
3124335.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V1186100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.598799.8%
 
?140.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common22557299.5%
 
Latin11860.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
43877517.2%
 
22796412.4%
 
82666911.8%
 
52605511.6%
 
7201628.9%
 
1196768.7%
 
0174977.8%
 
6163597.3%
 
9139816.2%
 
3124335.5%
 
.59872.7%
 
?14< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V1186100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII226758100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
43877517.1%
 
22796412.3%
 
82666911.8%
 
52605511.5%
 
7201628.9%
 
1196768.7%
 
0174977.7%
 
6163597.2%
 
9139816.2%
 
3124335.5%
 
.59872.6%
 
V11860.5%
 
?14< 0.1%
 

diag_2
Categorical

HIGH CARDINALITY

Distinct701
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
276
 
4744
428
 
4689
250
 
4218
427
 
3528
401
 
2663
Other values (696)
51570 
ValueCountFrequency (%) 
27647446.6%
 
42846896.6%
 
25042185.9%
 
42735284.9%
 
40126633.7%
 
49623343.3%
 
59923293.3%
 
40319912.8%
 
41418702.6%
 
41117352.4%
 
250.0214562.0%
 
70713972.0%
 
58513351.9%
 
58411521.6%
 
49110801.5%
 
250.0110701.5%
 
28510681.5%
 
78010351.4%
 
42510221.4%
 
6829901.4%
 
5189491.3%
 
4869451.3%
 
4247641.1%
 
4137431.0%
 
4936170.9%
 
Other values (676)2568836.0%
 
2020-12-10T03:00:22.481228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique130 ?
Unique (%)0.2%
2020-12-10T03:00:22.787310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.165196326
Min length1

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
43597015.9%
 
23489315.4%
 
52682511.9%
 
02385510.6%
 
8201108.9%
 
7200468.9%
 
1183538.1%
 
9153446.8%
 
6140386.2%
 
398904.4%
 
.46782.1%
 
V12670.6%
 
E5230.2%
 
?2410.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number21932497.0%
 
Other Punctuation49192.2%
 
Uppercase Letter17900.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
43597016.4%
 
23489315.9%
 
52682512.2%
 
02385510.9%
 
8201109.2%
 
7200469.1%
 
1183538.4%
 
9153447.0%
 
6140386.4%
 
398904.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.467895.1%
 
?2414.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V126770.8%
 
E52329.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common22424399.2%
 
Latin17900.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
43597016.0%
 
23489315.6%
 
52682512.0%
 
02385510.6%
 
8201109.0%
 
7200468.9%
 
1183538.2%
 
9153446.8%
 
6140386.3%
 
398904.4%
 
.46782.1%
 
?2410.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V126770.8%
 
E52329.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII226033100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
43597015.9%
 
23489315.4%
 
52682511.9%
 
02385510.6%
 
8201108.9%
 
7200468.9%
 
1183538.1%
 
9153446.8%
 
6140386.2%
 
398904.4%
 
.46782.1%
 
V12670.6%
 
E5230.2%
 
?2410.1%
 

diag_3
Categorical

HIGH CARDINALITY

Distinct748
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
250
8130 
401
5841 
276
 
3639
428
 
3237
427
 
2780
Other values (743)
47785 
ValueCountFrequency (%) 
250813011.4%
 
40158418.2%
 
27636395.1%
 
42832374.5%
 
42727803.9%
 
41425743.6%
 
49617952.5%
 
40316602.3%
 
27213992.0%
 
58513881.9%
 
59913631.9%
 
V459881.4%
 
?9821.4%
 
250.029691.4%
 
7079511.3%
 
7809111.3%
 
2858381.2%
 
4257891.1%
 
4247471.0%
 
250.67321.0%
 
5846760.9%
 
250.016490.9%
 
3056420.9%
 
6826310.9%
 
5186080.9%
 
Other values (723)2649337.1%
 
2020-12-10T03:00:23.187799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique121 ?
Unique (%)0.2%
2020-12-10T03:00:23.573380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.111759928
Min length1

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
23607416.2%
 
43445815.5%
 
52893413.0%
 
02793712.6%
 
7186228.4%
 
1173307.8%
 
8167107.5%
 
9121405.5%
 
6115145.2%
 
3100244.5%
 
.39171.8%
 
V27051.2%
 
?9820.4%
 
E8700.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number21374396.2%
 
Other Punctuation48992.2%
 
Uppercase Letter35751.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
23607416.9%
 
43445816.1%
 
52893413.5%
 
02793713.1%
 
7186228.7%
 
1173308.1%
 
8167107.8%
 
9121405.7%
 
6115145.4%
 
3100244.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V270575.7%
 
E87024.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.391780.0%
 
?98220.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common21864298.4%
 
Latin35751.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
23607416.5%
 
43445815.8%
 
52893413.2%
 
02793712.8%
 
7186228.5%
 
1173307.9%
 
8167107.6%
 
9121405.6%
 
6115145.3%
 
3100244.6%
 
.39171.8%
 
?9820.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V270575.7%
 
E87024.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII222217100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
23607416.2%
 
43445815.5%
 
52893413.0%
 
02793712.6%
 
7186228.4%
 
1173307.8%
 
8167107.5%
 
9121405.5%
 
6115145.2%
 
3100244.5%
 
.39171.8%
 
V27051.2%
 
?9820.4%
 
E8700.4%
 

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.429157565
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2020-12-10T03:00:23.908427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933391854
Coefficient of variation (CV)0.2602437541
Kurtosis-0.05462390936
Mean7.429157565
Median Absolute Deviation (MAD)1
Skewness-0.8699717887
Sum530531
Variance3.738004063
MonotocityNot monotonic
2020-12-10T03:00:24.121006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
93482448.8%
 
5798611.2%
 
8742810.4%
 
7726010.2%
 
6710610.0%
 
438785.4%
 
319732.8%
 
27161.0%
 
11460.2%
 
16370.1%
 
1014< 0.1%
 
1313< 0.1%
 
129< 0.1%
 
119< 0.1%
 
147< 0.1%
 
156< 0.1%
 
ValueCountFrequency (%) 
11460.2%
 
27161.0%
 
319732.8%
 
438785.4%
 
5798611.2%
 
6710610.0%
 
7726010.2%
 
8742810.4%
 
93482448.8%
 
1014< 0.1%
 
ValueCountFrequency (%) 
16370.1%
 
156< 0.1%
 
147< 0.1%
 
1313< 0.1%
 
129< 0.1%
 
119< 0.1%
 
1014< 0.1%
 
93482448.8%
 
8742810.4%
 
7726010.2%
 

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
None
67671 
Norm
 
1832
>200
 
1012
>300
 
897
ValueCountFrequency (%) 
None6767194.8%
 
Norm18322.6%
 
>20010121.4%
 
>3008971.3%
 
2020-12-10T03:00:24.368195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:24.522834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:24.686225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N6950324.3%
 
o6950324.3%
 
n6767123.7%
 
e6767123.7%
 
038181.3%
 
>19090.7%
 
r18320.6%
 
m18320.6%
 
210120.4%
 
38970.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter20850973.0%
 
Uppercase Letter6950324.3%
 
Decimal Number57272.0%
 
Math Symbol19090.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N69503100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6950333.3%
 
n6767132.5%
 
e6767132.5%
 
r18320.9%
 
m18320.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>1909100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0381866.7%
 
2101217.7%
 
389715.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin27801297.3%
 
Common76362.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N6950325.0%
 
o6950325.0%
 
n6767124.3%
 
e6767124.3%
 
r18320.7%
 
m18320.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
0381850.0%
 
>190925.0%
 
2101213.3%
 
389711.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII285648100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N6950324.3%
 
o6950324.3%
 
n6767123.7%
 
e6767123.7%
 
038181.3%
 
>19090.7%
 
r18320.6%
 
m18320.6%
 
210120.4%
 
38970.3%
 

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
None
59468 
>8
 
5766
Norm
 
3503
>7
 
2675
ValueCountFrequency (%) 
None5946883.3%
 
>857668.1%
 
Norm35034.9%
 
>726753.7%
 
2020-12-10T03:00:24.930116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:25.183383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:25.378596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.763597155
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N6297123.4%
 
o6297123.4%
 
n5946822.1%
 
e5946822.1%
 
>84413.1%
 
857662.1%
 
r35031.3%
 
m35031.3%
 
726751.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter18891370.3%
 
Uppercase Letter6297123.4%
 
Math Symbol84413.1%
 
Decimal Number84413.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N62971100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6297133.3%
 
n5946831.5%
 
e5946831.5%
 
r35031.9%
 
m35031.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>8441100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
8576668.3%
 
7267531.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin25188493.7%
 
Common168826.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N6297125.0%
 
o6297125.0%
 
n5946823.6%
 
e5946823.6%
 
r35031.4%
 
m35031.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
>844150.0%
 
8576634.2%
 
7267515.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII268766100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N6297123.4%
 
o6297123.4%
 
n5946822.1%
 
e5946822.1%
 
>84413.1%
 
857662.1%
 
r35031.3%
 
m35031.3%
 
726751.0%
 

metformin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
57341 
Steady
12939 
Up
 
754
Down
 
378
ValueCountFrequency (%) 
No5734180.3%
 
Steady1293918.1%
 
Up7541.1%
 
Down3780.5%
 
2020-12-10T03:00:25.612354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:25.753709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:25.939767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.735338599
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5771929.5%
 
N5734129.4%
 
S129396.6%
 
t129396.6%
 
e129396.6%
 
a129396.6%
 
d129396.6%
 
y129396.6%
 
U7540.4%
 
p7540.4%
 
D3780.2%
 
w3780.2%
 
n3780.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12392463.4%
 
Uppercase Letter7141236.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N5734180.3%
 
S1293918.1%
 
U7541.1%
 
D3780.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5771946.6%
 
t1293910.4%
 
e1293910.4%
 
a1293910.4%
 
d1293910.4%
 
y1293910.4%
 
p7540.6%
 
w3780.3%
 
n3780.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin195336100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5771929.5%
 
N5734129.4%
 
S129396.6%
 
t129396.6%
 
e129396.6%
 
a129396.6%
 
d129396.6%
 
y129396.6%
 
U7540.4%
 
p7540.4%
 
D3780.2%
 
w3780.2%
 
n3780.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII195336100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5771929.5%
 
N5734129.4%
 
S129396.6%
 
t129396.6%
 
e129396.6%
 
a129396.6%
 
d129396.6%
 
y129396.6%
 
U7540.4%
 
p7540.4%
 
D3780.2%
 
w3780.2%
 
n3780.2%
 

repaglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
70373 
Steady
 
931
Up
 
73
Down
 
35
ValueCountFrequency (%) 
No7037398.5%
 
Steady9311.3%
 
Up730.1%
 
Down35< 0.1%
 
2020-12-10T03:00:26.221737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:26.398159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:26.603777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.053128326
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7040848.0%
 
N7037348.0%
 
S9310.6%
 
t9310.6%
 
e9310.6%
 
a9310.6%
 
d9310.6%
 
y9310.6%
 
U73< 0.1%
 
p73< 0.1%
 
D35< 0.1%
 
w35< 0.1%
 
n35< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7520651.3%
 
Uppercase Letter7141248.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N7037398.5%
 
S9311.3%
 
U730.1%
 
D35< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7040893.6%
 
t9311.2%
 
e9311.2%
 
a9311.2%
 
d9311.2%
 
y9311.2%
 
p730.1%
 
w35< 0.1%
 
n35< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin146618100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7040848.0%
 
N7037348.0%
 
S9310.6%
 
t9310.6%
 
e9310.6%
 
a9310.6%
 
d9310.6%
 
y9310.6%
 
U73< 0.1%
 
p73< 0.1%
 
D35< 0.1%
 
w35< 0.1%
 
n35< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII146618100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7040848.0%
 
N7037348.0%
 
S9310.6%
 
t9310.6%
 
e9310.6%
 
a9310.6%
 
d9310.6%
 
y9310.6%
 
U73< 0.1%
 
p73< 0.1%
 
D35< 0.1%
 
w35< 0.1%
 
n35< 0.1%
 

nateglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
70900 
Steady
 
484
Up
 
19
Down
 
9
ValueCountFrequency (%) 
No7090099.3%
 
Steady4840.7%
 
Up19< 0.1%
 
Down9< 0.1%
 
2020-12-10T03:00:26.896066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:27.079367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:27.271743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.027362348
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7090949.0%
 
N7090049.0%
 
S4840.3%
 
t4840.3%
 
e4840.3%
 
a4840.3%
 
d4840.3%
 
y4840.3%
 
U19< 0.1%
 
p19< 0.1%
 
D9< 0.1%
 
w9< 0.1%
 
n9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7336650.7%
 
Uppercase Letter7141249.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N7090099.3%
 
S4840.7%
 
U19< 0.1%
 
D9< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7090996.7%
 
t4840.7%
 
e4840.7%
 
a4840.7%
 
d4840.7%
 
y4840.7%
 
p19< 0.1%
 
w9< 0.1%
 
n9< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin144778100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7090949.0%
 
N7090049.0%
 
S4840.3%
 
t4840.3%
 
e4840.3%
 
a4840.3%
 
d4840.3%
 
y4840.3%
 
U19< 0.1%
 
p19< 0.1%
 
D9< 0.1%
 
w9< 0.1%
 
n9< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII144778100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7090949.0%
 
N7090049.0%
 
S4840.3%
 
t4840.3%
 
e4840.3%
 
a4840.3%
 
d4840.3%
 
y4840.3%
 
U19< 0.1%
 
p19< 0.1%
 
D9< 0.1%
 
w9< 0.1%
 
n9< 0.1%
 

chlorpropamide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71357 
Steady
 
50
Up
 
4
Down
 
1
ValueCountFrequency (%) 
No7135799.9%
 
Steady500.1%
 
Up4< 0.1%
 
Down1< 0.1%
 
2020-12-10T03:00:27.567861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:27.770561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:27.988016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.002828656
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7135849.9%
 
N7135749.9%
 
S50< 0.1%
 
t50< 0.1%
 
e50< 0.1%
 
a50< 0.1%
 
d50< 0.1%
 
y50< 0.1%
 
U4< 0.1%
 
p4< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7161450.1%
 
Uppercase Letter7141249.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N7135799.9%
 
S500.1%
 
U4< 0.1%
 
D1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7135899.6%
 
t500.1%
 
e500.1%
 
a500.1%
 
d500.1%
 
y500.1%
 
p4< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin143026100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7135849.9%
 
N7135749.9%
 
S50< 0.1%
 
t50< 0.1%
 
e50< 0.1%
 
a50< 0.1%
 
d50< 0.1%
 
y50< 0.1%
 
U4< 0.1%
 
p4< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII143026100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7135849.9%
 
N7135749.9%
 
S50< 0.1%
 
t50< 0.1%
 
e50< 0.1%
 
a50< 0.1%
 
d50< 0.1%
 
y50< 0.1%
 
U4< 0.1%
 
p4< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

glimepiride
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
67810 
Steady
 
3251
Up
 
219
Down
 
132
ValueCountFrequency (%) 
No6781095.0%
 
Steady32514.6%
 
Up2190.3%
 
Down1320.2%
 
2020-12-10T03:00:28.279759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:28.428950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:28.616008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.185795104
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6794243.5%
 
N6781043.4%
 
S32512.1%
 
t32512.1%
 
e32512.1%
 
a32512.1%
 
d32512.1%
 
y32512.1%
 
U2190.1%
 
p2190.1%
 
D1320.1%
 
w1320.1%
 
n1320.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8468054.3%
 
Uppercase Letter7141245.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N6781095.0%
 
S32514.6%
 
U2190.3%
 
D1320.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6794280.2%
 
t32513.8%
 
e32513.8%
 
a32513.8%
 
d32513.8%
 
y32513.8%
 
p2190.3%
 
w1320.2%
 
n1320.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin156092100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6794243.5%
 
N6781043.4%
 
S32512.1%
 
t32512.1%
 
e32512.1%
 
a32512.1%
 
d32512.1%
 
y32512.1%
 
U2190.1%
 
p2190.1%
 
D1320.1%
 
w1320.1%
 
n1320.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII156092100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6794243.5%
 
N6781043.4%
 
S32512.1%
 
t32512.1%
 
e32512.1%
 
a32512.1%
 
d32512.1%
 
y32512.1%
 
U2190.1%
 
p2190.1%
 
D1320.1%
 
w1320.1%
 
n1320.1%
 

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71411 
Steady
 
1
ValueCountFrequency (%) 
No71411> 99.9%
 
Steady1< 0.1%
 
2020-12-10T03:00:28.839801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:28.994994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:29.152112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000056013
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7141650.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71411> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71411> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142828100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142828100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

glipizide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
62511 
Steady
7992 
Up
 
525
Down
 
384
ValueCountFrequency (%) 
No6251187.5%
 
Steady799211.2%
 
Up5250.7%
 
Down3840.5%
 
2020-12-10T03:00:29.451895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:29.653734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:29.816833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.458410351
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6289535.8%
 
N6251135.6%
 
S79924.6%
 
t79924.6%
 
e79924.6%
 
a79924.6%
 
d79924.6%
 
y79924.6%
 
U5250.3%
 
p5250.3%
 
D3840.2%
 
w3840.2%
 
n3840.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10414859.3%
 
Uppercase Letter7141240.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N6251187.5%
 
S799211.2%
 
U5250.7%
 
D3840.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6289560.4%
 
t79927.7%
 
e79927.7%
 
a79927.7%
 
d79927.7%
 
y79927.7%
 
p5250.5%
 
w3840.4%
 
n3840.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin175560100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6289535.8%
 
N6251135.6%
 
S79924.6%
 
t79924.6%
 
e79924.6%
 
a79924.6%
 
d79924.6%
 
y79924.6%
 
U5250.3%
 
p5250.3%
 
D3840.2%
 
w3840.2%
 
n3840.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII175560100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6289535.8%
 
N6251135.6%
 
S79924.6%
 
t79924.6%
 
e79924.6%
 
a79924.6%
 
d79924.6%
 
y79924.6%
 
U5250.3%
 
p5250.3%
 
D3840.2%
 
w3840.2%
 
n3840.2%
 

glyburide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
63956 
Steady
6516 
Up
 
566
Down
 
374
ValueCountFrequency (%) 
No6395689.6%
 
Steady65169.1%
 
Up5660.8%
 
Down3740.5%
 
2020-12-10T03:00:30.083871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:30.282398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:30.495842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.375455106
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6433037.9%
 
N6395637.7%
 
S65163.8%
 
t65163.8%
 
e65163.8%
 
a65163.8%
 
d65163.8%
 
y65163.8%
 
U5660.3%
 
p5660.3%
 
D3740.2%
 
w3740.2%
 
n3740.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter9822457.9%
 
Uppercase Letter7141242.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N6395689.6%
 
S65169.1%
 
U5660.8%
 
D3740.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6433065.5%
 
t65166.6%
 
e65166.6%
 
a65166.6%
 
d65166.6%
 
y65166.6%
 
p5660.6%
 
w3740.4%
 
n3740.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin169636100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6433037.9%
 
N6395637.7%
 
S65163.8%
 
t65163.8%
 
e65163.8%
 
a65163.8%
 
d65163.8%
 
y65163.8%
 
U5660.3%
 
p5660.3%
 
D3740.2%
 
w3740.2%
 
n3740.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII169636100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6433037.9%
 
N6395637.7%
 
S65163.8%
 
t65163.8%
 
e65163.8%
 
a65163.8%
 
d65163.8%
 
y65163.8%
 
U5660.3%
 
p5660.3%
 
D3740.2%
 
w3740.2%
 
n3740.2%
 

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71399 
Steady
 
13
ValueCountFrequency (%) 
No71399> 99.9%
 
Steady13< 0.1%
 
2020-12-10T03:00:30.767709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:30.962755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:31.174262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000728169
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7139950.0%
 
o7139950.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7146450.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71399> 99.9%
 
S13< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7139999.9%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142876100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7139950.0%
 
o7139950.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142876100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7139950.0%
 
o7139950.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

pioglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
66354 
Steady
 
4826
Up
 
154
Down
 
78
ValueCountFrequency (%) 
No6635492.9%
 
Steady48266.8%
 
Up1540.2%
 
Down780.1%
 
2020-12-10T03:00:31.452066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:31.596436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:31.779958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.272503221
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6643240.9%
 
N6635440.9%
 
S48263.0%
 
t48263.0%
 
e48263.0%
 
a48263.0%
 
d48263.0%
 
y48263.0%
 
U1540.1%
 
p1540.1%
 
D78< 0.1%
 
w78< 0.1%
 
n78< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter9087256.0%
 
Uppercase Letter7141244.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N6635492.9%
 
S48266.8%
 
U1540.2%
 
D780.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6643273.1%
 
t48265.3%
 
e48265.3%
 
a48265.3%
 
d48265.3%
 
y48265.3%
 
p1540.2%
 
w780.1%
 
n780.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin162284100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6643240.9%
 
N6635440.9%
 
S48263.0%
 
t48263.0%
 
e48263.0%
 
a48263.0%
 
d48263.0%
 
y48263.0%
 
U1540.1%
 
p1540.1%
 
D78< 0.1%
 
w78< 0.1%
 
n78< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162284100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6643240.9%
 
N6635440.9%
 
S48263.0%
 
t48263.0%
 
e48263.0%
 
a48263.0%
 
d48263.0%
 
y48263.0%
 
U1540.1%
 
p1540.1%
 
D78< 0.1%
 
w78< 0.1%
 
n78< 0.1%
 

rosiglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
66987 
Steady
 
4241
Up
 
124
Down
 
60
ValueCountFrequency (%) 
No6698793.8%
 
Steady42415.9%
 
Up1240.2%
 
Down600.1%
 
2020-12-10T03:00:32.012557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:32.168704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:32.367328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.239231502
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6704741.9%
 
N6698741.9%
 
S42412.7%
 
t42412.7%
 
e42412.7%
 
a42412.7%
 
d42412.7%
 
y42412.7%
 
U1240.1%
 
p1240.1%
 
D60< 0.1%
 
w60< 0.1%
 
n60< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8849655.3%
 
Uppercase Letter7141244.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N6698793.8%
 
S42415.9%
 
U1240.2%
 
D600.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6704775.8%
 
t42414.8%
 
e42414.8%
 
a42414.8%
 
d42414.8%
 
y42414.8%
 
p1240.1%
 
w600.1%
 
n600.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin159908100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6704741.9%
 
N6698741.9%
 
S42412.7%
 
t42412.7%
 
e42412.7%
 
a42412.7%
 
d42412.7%
 
y42412.7%
 
U1240.1%
 
p1240.1%
 
D60< 0.1%
 
w60< 0.1%
 
n60< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII159908100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6704741.9%
 
N6698741.9%
 
S42412.7%
 
t42412.7%
 
e42412.7%
 
a42412.7%
 
d42412.7%
 
y42412.7%
 
U1240.1%
 
p1240.1%
 
D60< 0.1%
 
w60< 0.1%
 
n60< 0.1%
 

acarbose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71202 
Steady
 
201
Up
 
7
Down
 
2
ValueCountFrequency (%) 
No7120299.7%
 
Steady2010.3%
 
Up7< 0.1%
 
Down2< 0.1%
 
2020-12-10T03:00:32.614361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:32.766307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:32.963696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.011314625
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7120449.6%
 
N7120249.6%
 
S2010.1%
 
t2010.1%
 
e2010.1%
 
a2010.1%
 
d2010.1%
 
y2010.1%
 
U7< 0.1%
 
p7< 0.1%
 
D2< 0.1%
 
w2< 0.1%
 
n2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7222050.3%
 
Uppercase Letter7141249.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N7120299.7%
 
S2010.3%
 
U7< 0.1%
 
D2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7120498.6%
 
t2010.3%
 
e2010.3%
 
a2010.3%
 
d2010.3%
 
y2010.3%
 
p7< 0.1%
 
w2< 0.1%
 
n2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin143632100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7120449.6%
 
N7120249.6%
 
S2010.1%
 
t2010.1%
 
e2010.1%
 
a2010.1%
 
d2010.1%
 
y2010.1%
 
U7< 0.1%
 
p7< 0.1%
 
D2< 0.1%
 
w2< 0.1%
 
n2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII143632100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7120449.6%
 
N7120249.6%
 
S2010.1%
 
t2010.1%
 
e2010.1%
 
a2010.1%
 
d2010.1%
 
y2010.1%
 
U7< 0.1%
 
p7< 0.1%
 
D2< 0.1%
 
w2< 0.1%
 
n2< 0.1%
 

miglitol
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71385 
Steady
 
20
Down
 
5
Up
 
2
ValueCountFrequency (%) 
No71385> 99.9%
 
Steady20< 0.1%
 
Down5< 0.1%
 
Up2< 0.1%
 
2020-12-10T03:00:33.203798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:33.352204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:33.612134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001260292
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7139050.0%
 
N7138549.9%
 
S20< 0.1%
 
t20< 0.1%
 
e20< 0.1%
 
a20< 0.1%
 
d20< 0.1%
 
y20< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7150250.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71385> 99.9%
 
S20< 0.1%
 
D5< 0.1%
 
U2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7139099.8%
 
t20< 0.1%
 
e20< 0.1%
 
a20< 0.1%
 
d20< 0.1%
 
y20< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
p2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142914100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7139050.0%
 
N7138549.9%
 
S20< 0.1%
 
t20< 0.1%
 
e20< 0.1%
 
a20< 0.1%
 
d20< 0.1%
 
y20< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142914100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7139050.0%
 
N7138549.9%
 
S20< 0.1%
 
t20< 0.1%
 
e20< 0.1%
 
a20< 0.1%
 
d20< 0.1%
 
y20< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71410 
Steady
 
2
ValueCountFrequency (%) 
No71410> 99.9%
 
Steady2< 0.1%
 
2020-12-10T03:00:33.891883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:34.039919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:34.255936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000112026
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7142050.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71410> 99.9%
 
S2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71410> 99.9%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142832100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142832100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

tolazamide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71385 
Steady
 
26
Up
 
1
ValueCountFrequency (%) 
No71385> 99.9%
 
Steady26< 0.1%
 
Up1< 0.1%
 
2020-12-10T03:00:34.539759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:34.688702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:34.856610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001456338
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7138549.9%
 
o7138549.9%
 
S26< 0.1%
 
t26< 0.1%
 
e26< 0.1%
 
a26< 0.1%
 
d26< 0.1%
 
y26< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7151650.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71385> 99.9%
 
S26< 0.1%
 
U1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7138599.8%
 
t26< 0.1%
 
e26< 0.1%
 
a26< 0.1%
 
d26< 0.1%
 
y26< 0.1%
 
p1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142928100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7138549.9%
 
o7138549.9%
 
S26< 0.1%
 
t26< 0.1%
 
e26< 0.1%
 
a26< 0.1%
 
d26< 0.1%
 
y26< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142928100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7138549.9%
 
o7138549.9%
 
S26< 0.1%
 
t26< 0.1%
 
e26< 0.1%
 
a26< 0.1%
 
d26< 0.1%
 
y26< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

examide
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71412 
ValueCountFrequency (%) 
No71412100.0%
 
2020-12-10T03:00:35.083678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:35.230505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:35.367809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7141250.0%
 
Lowercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71412100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71412100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142824100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142824100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

citoglipton
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71412 
ValueCountFrequency (%) 
No71412100.0%
 
2020-12-10T03:00:35.556981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:35.718342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:35.855795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7141250.0%
 
Lowercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71412100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71412100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142824100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142824100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

insulin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
33287 
Steady
21649 
Down
8518 
Up
7958 
ValueCountFrequency (%) 
No3328746.6%
 
Steady2164930.3%
 
Down851811.9%
 
Up795811.1%
 
2020-12-10T03:00:36.079911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:37.147695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:37.339669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length3.451184675
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o4180517.0%
 
N3328713.5%
 
S216498.8%
 
t216498.8%
 
e216498.8%
 
a216498.8%
 
d216498.8%
 
y216498.8%
 
D85183.5%
 
w85183.5%
 
n85183.5%
 
U79583.2%
 
p79583.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter17504471.0%
 
Uppercase Letter7141229.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3328746.6%
 
S2164930.3%
 
D851811.9%
 
U795811.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o4180523.9%
 
t2164912.4%
 
e2164912.4%
 
a2164912.4%
 
d2164912.4%
 
y2164912.4%
 
w85184.9%
 
n85184.9%
 
p79584.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin246456100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o4180517.0%
 
N3328713.5%
 
S216498.8%
 
t216498.8%
 
e216498.8%
 
a216498.8%
 
d216498.8%
 
y216498.8%
 
D85183.5%
 
w85183.5%
 
n85183.5%
 
U79583.2%
 
p79583.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII246456100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o4180517.0%
 
N3328713.5%
 
S216498.8%
 
t216498.8%
 
e216498.8%
 
a216498.8%
 
d216498.8%
 
y216498.8%
 
D85183.5%
 
w85183.5%
 
n85183.5%
 
U79583.2%
 
p79583.2%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
70909 
Steady
 
496
Down
 
4
Up
 
3
ValueCountFrequency (%) 
No7090999.3%
 
Steady4960.7%
 
Down4< 0.1%
 
Up3< 0.1%
 
2020-12-10T03:00:37.599787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:37.787872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:37.980102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.027894472
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o7091349.0%
 
N7090949.0%
 
S4960.3%
 
t4960.3%
 
e4960.3%
 
a4960.3%
 
d4960.3%
 
y4960.3%
 
D4< 0.1%
 
w4< 0.1%
 
n4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7340450.7%
 
Uppercase Letter7141249.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N7090999.3%
 
S4960.7%
 
D4< 0.1%
 
U3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7091396.6%
 
t4960.7%
 
e4960.7%
 
a4960.7%
 
d4960.7%
 
y4960.7%
 
w4< 0.1%
 
n4< 0.1%
 
p3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin144816100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o7091349.0%
 
N7090949.0%
 
S4960.3%
 
t4960.3%
 
e4960.3%
 
a4960.3%
 
d4960.3%
 
y4960.3%
 
D4< 0.1%
 
w4< 0.1%
 
n4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII144816100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o7091349.0%
 
N7090949.0%
 
S4960.3%
 
t4960.3%
 
e4960.3%
 
a4960.3%
 
d4960.3%
 
y4960.3%
 
D4< 0.1%
 
w4< 0.1%
 
n4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71404 
Steady
 
8
ValueCountFrequency (%) 
No71404> 99.9%
 
Steady8< 0.1%
 
2020-12-10T03:00:38.269531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:38.407986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:38.568258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000448104
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7140450.0%
 
o7140450.0%
 
S8< 0.1%
 
t8< 0.1%
 
e8< 0.1%
 
a8< 0.1%
 
d8< 0.1%
 
y8< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7144450.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71404> 99.9%
 
S8< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o7140499.9%
 
t8< 0.1%
 
e8< 0.1%
 
a8< 0.1%
 
d8< 0.1%
 
y8< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142856100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7140450.0%
 
o7140450.0%
 
S8< 0.1%
 
t8< 0.1%
 
e8< 0.1%
 
a8< 0.1%
 
d8< 0.1%
 
y8< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142856100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7140450.0%
 
o7140450.0%
 
S8< 0.1%
 
t8< 0.1%
 
e8< 0.1%
 
a8< 0.1%
 
d8< 0.1%
 
y8< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71411 
Steady
 
1
ValueCountFrequency (%) 
No71411> 99.9%
 
Steady1< 0.1%
 
2020-12-10T03:00:38.828818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-10T03:00:38.981735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:39.130471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000056013
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7141650.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71411> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71411> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142828100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142828100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141150.0%
 
o7141150.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71410 
Steady
 
2
ValueCountFrequency (%) 
No71410> 99.9%
 
Steady2< 0.1%
 
2020-12-10T03:00:39.391826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:39.540033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:39.696106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000112026
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7142050.0%
 
Uppercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71410> 99.9%
 
S2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71410> 99.9%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142832100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142832100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141050.0%
 
o7141050.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

metformin.pioglitazone
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
71412 
ValueCountFrequency (%) 
No71412100.0%
 
2020-12-10T03:00:39.956123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:40.129412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:40.256131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7141250.0%
 
Lowercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N71412100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o71412100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142824100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142824100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N7141250.0%
 
o7141250.0%
 

change
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No
38454 
Ch
32958 
ValueCountFrequency (%) 
No3845453.8%
 
Ch3295846.2%
 
2020-12-10T03:00:40.463802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-10T03:00:40.614565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:40.771696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N3845426.9%
 
o3845426.9%
 
C3295823.1%
 
h3295823.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7141250.0%
 
Lowercase Letter7141250.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3845453.8%
 
C3295846.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o3845453.8%
 
h3295846.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin142824100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N3845426.9%
 
o3845426.9%
 
C3295823.1%
 
h3295823.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142824100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N3845426.9%
 
o3845426.9%
 
C3295823.1%
 
h3295823.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Yes
54881 
No
16531 
ValueCountFrequency (%) 
Yes5488176.9%
 
No1653123.1%
 
2020-12-10T03:00:40.904515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

readmitted
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
38717 
1
32695 
ValueCountFrequency (%) 
03871754.2%
 
13269545.8%
 
2020-12-10T03:00:40.979686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-10T02:59:30.387199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:30.637976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:30.859956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:31.096839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:31.341761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:31.605656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:31.825972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:32.053198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:32.289015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:32.518792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:32.757028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:32.989046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:33.229860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:33.425313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:33.637509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:33.837135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:34.049538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:34.252776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:34.476144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:34.725232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:34.950049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:35.196252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:35.435398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:35.659256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:35.869980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:36.116856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:36.348297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:36.586608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:36.819243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:37.056014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:37.303311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:37.548005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:37.799020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:38.048651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:38.289713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:38.496830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:38.710013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:38.923383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:39.203459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:39.423584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:39.673551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:39.899161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:40.127244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:40.368227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:40.622037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:40.889355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:41.117020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:41.348471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:41.580336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:41.806347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:42.029445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:42.287291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:42.524284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:42.736801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:42.957158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:43.183966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:43.416598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:43.655810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:43.878085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:44.090979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:44.319608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:44.544744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:44.768610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:46.120648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:46.347243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:46.627603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:46.870504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:47.124094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:47.380985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:47.622311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:47.868040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:48.143655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:48.411052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:48.674051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:48.936424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:49.186249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:49.413411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:49.661207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:49.918639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:50.136638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:50.410525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:50.687784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:50.952426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:51.256296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:51.576195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:51.826671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:52.080625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:52.315588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:52.569054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:52.823379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:53.061531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:53.320495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:53.548525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:53.792510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:54.033695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:54.262119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:54.491070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:54.724152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:54.988901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:55.245136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:55.508355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:55.773383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:56.032088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:56.283886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:56.533457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:56.793556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:57.058271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:57.361713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:57.608695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:57.837525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:58.075274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:58.297062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:58.514496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:58.737271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:58.961293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:59.188353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:59.405169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:59.637549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T02:59:59.879131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:00.117914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:00.356621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-10T03:00:41.115695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-10T03:00:41.505704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-10T03:00:41.912933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-10T03:00:42.378620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-10T03:00:43.152241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-10T03:00:01.950027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-10T03:00:06.187260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

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Last rows

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